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The stochastic differential equation (SDE)-based random process models ofvolatile renewable energy sources (RESs) jointly capture the evolvingprobability distribution and temporal correlation in continuous time. It hasenabled recent studies to remarkably improve the performance of power systemdynamic uncertainty quantification and optimization. However, considering thenon-homogeneous random process nature of PV, there still remains a challengingquestion: how can a realistic and accurate SDE model for PV power be obtainedthat reflects its weather-dependent uncertainty in online operation, especiallywhen high-resolution numerical weather prediction (NWP) is unavailable for manydistributed plants? To fill this gap, this article finds that an accurate SDEmodel for PV power can be constructed by only using the cheap data fromlow-resolution public weather reports. Specifically, an hourly parameterizedJacobi diffusion process is constructed to recreate the temporal patterns of PVvolatility during a day. Its parameters are mapped from the public weatherreport using an ensemble of extreme learning machines (ELMs) to reflect thevarying weather conditions. The SDE model jointly captures intraday andintrahour volatility. Statistical examination based on real-world datacollected in Macau shows the proposed approach outperforms a selection ofstate-of-the-art deep learning-based time-series forecast methods.

Achieving an Accurate Random Process Model for PV Power using Cheap Data: Leveraging the SDE and Public Weather Reports